Overview

Dataset statistics

Number of variables14
Number of observations95339
Missing cells190678
Missing cells (%)14.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 MiB
Average record size in memory105.0 B

Variable types

Numeric9
Categorical2
Unsupported2
Boolean1

Alerts

Production is highly overall correlated with num_sizes and 3 other fieldsHigh correlation
id_season is highly overall correlated with yearHigh correlation
num_sizes is highly overall correlated with Production and 3 other fieldsHigh correlation
num_stores is highly overall correlated with Production and 3 other fieldsHigh correlation
weekly_demand is highly overall correlated with Production and 3 other fieldsHigh correlation
weekly_sales is highly overall correlated with Production and 3 other fieldsHigh correlation
year is highly overall correlated with id_seasonHigh correlation
has_plus_sizes is highly imbalanced (51.7%)Imbalance
heel_shape_type has 95339 (100.0%) missing valuesMissing
toecap_type has 95339 (100.0%) missing valuesMissing
heel_shape_type is an unsupported type, check if it needs cleaning or further analysisUnsupported
toecap_type is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2025-11-15 11:52:15.991027
Analysis finished2025-11-15 11:52:32.088822
Duration16.1 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct9843
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6373.2094
Minimum1
Maximum12767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:32.240725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile631
Q13205
median6379
Q39515.5
95-th percentile12140
Maximum12767
Range12766
Interquartile range (IQR)6310.5

Descriptive statistics

Standard deviation3681.0071
Coefficient of variation (CV)0.57757511
Kurtosis-1.1881897
Mean6373.2094
Median Absolute Deviation (MAD)3158
Skewness0.0054325096
Sum6.0761541 × 108
Variance13549813
MonotonicityIncreasing
2025-11-15T12:52:32.489473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11792 31
 
< 0.1%
7845 31
 
< 0.1%
6056 31
 
< 0.1%
2520 31
 
< 0.1%
983 31
 
< 0.1%
5667 30
 
< 0.1%
3476 30
 
< 0.1%
3596 30
 
< 0.1%
5100 30
 
< 0.1%
2139 30
 
< 0.1%
Other values (9833) 95034
99.7%
ValueCountFrequency (%)
1 12
< 0.1%
2 12
< 0.1%
3 18
< 0.1%
4 8
< 0.1%
6 8
< 0.1%
7 12
< 0.1%
8 6
 
< 0.1%
10 6
 
< 0.1%
11 8
< 0.1%
12 8
< 0.1%
ValueCountFrequency (%)
12767 10
< 0.1%
12765 8
< 0.1%
12764 16
< 0.1%
12763 8
< 0.1%
12762 4
 
< 0.1%
12761 12
< 0.1%
12760 16
< 0.1%
12759 12
< 0.1%
12758 8
< 0.1%
12757 10
< 0.1%

id_season
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size745.0 KiB
87
25265 
89
24080 
86
23691 
88
22303 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters190678
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row86
2nd row86
3rd row86
4th row86
5th row86

Common Values

ValueCountFrequency (%)
87 25265
26.5%
89 24080
25.3%
86 23691
24.8%
88 22303
23.4%

Length

2025-11-15T12:52:32.700872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-15T12:52:32.850327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
87 25265
26.5%
89 24080
25.3%
86 23691
24.8%
88 22303
23.4%

Most occurring characters

ValueCountFrequency (%)
8 117642
61.7%
7 25265
 
13.3%
9 24080
 
12.6%
6 23691
 
12.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 190678
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 117642
61.7%
7 25265
 
13.3%
9 24080
 
12.6%
6 23691
 
12.4%

Most occurring scripts

ValueCountFrequency (%)
Common 190678
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 117642
61.7%
7 25265
 
13.3%
9 24080
 
12.6%
6 23691
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190678
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 117642
61.7%
7 25265
 
13.3%
9 24080
 
12.6%
6 23691
 
12.4%

heel_shape_type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing95339
Missing (%)100.0%
Memory size745.0 KiB

toecap_type
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing95339
Missing (%)100.0%
Memory size745.0 KiB

life_cycle_length
Real number (ℝ)

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.164004
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:33.028747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median10
Q312
95-th percentile24
Maximum31
Range30
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.7427803
Coefficient of variation (CV)0.42482788
Kurtosis2.3248152
Mean11.164004
Median Absolute Deviation (MAD)2
Skewness1.5288402
Sum1064365
Variance22.493965
MonotonicityNot monotonic
2025-11-15T12:52:33.248836image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
8 35792
37.5%
12 22146
23.2%
10 9520
 
10.0%
16 7408
 
7.8%
6 6876
 
7.2%
24 4392
 
4.6%
14 2716
 
2.8%
20 1860
 
2.0%
18 1422
 
1.5%
4 1308
 
1.4%
Other values (15) 1899
 
2.0%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
4 1308
 
1.4%
5 10
 
< 0.1%
6 6876
 
7.2%
8 35792
37.5%
10 9520
 
10.0%
12 22146
23.2%
13 52
 
0.1%
14 2716
 
2.8%
ValueCountFrequency (%)
31 155
 
0.2%
30 450
 
0.5%
28 28
 
< 0.1%
26 286
 
0.3%
25 100
 
0.1%
24 4392
4.6%
23 23
 
< 0.1%
22 308
 
0.3%
21 126
 
0.1%
20 1860
2.0%

num_stores
Real number (ℝ)

HIGH CORRELATION 

Distinct1242
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean531.35677
Minimum8
Maximum1272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:33.482149image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile56
Q1146
median464
Q3893
95-th percentile1179
Maximum1272
Range1264
Interquartile range (IQR)747

Descriptive statistics

Standard deviation393.192
Coefficient of variation (CV)0.7399774
Kurtosis-1.3367463
Mean531.35677
Median Absolute Deviation (MAD)342
Skewness0.3367228
Sum50659023
Variance154599.95
MonotonicityNot monotonic
2025-11-15T12:52:33.718681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 342
 
0.4%
132 330
 
0.3%
92 320
 
0.3%
113 314
 
0.3%
114 308
 
0.3%
111 308
 
0.3%
104 304
 
0.3%
125 292
 
0.3%
90 288
 
0.3%
65 284
 
0.3%
Other values (1232) 92249
96.8%
ValueCountFrequency (%)
8 6
 
< 0.1%
9 4
 
< 0.1%
10 1
 
< 0.1%
11 33
< 0.1%
12 10
 
< 0.1%
14 8
 
< 0.1%
15 18
< 0.1%
16 44
< 0.1%
17 10
 
< 0.1%
18 18
< 0.1%
ValueCountFrequency (%)
1272 16
 
< 0.1%
1270 44
< 0.1%
1268 16
 
< 0.1%
1265 16
 
< 0.1%
1255 18
 
< 0.1%
1250 18
 
< 0.1%
1249 8
 
< 0.1%
1248 74
0.1%
1247 10
 
< 0.1%
1245 16
 
< 0.1%

num_sizes
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6762395
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:33.907368image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median7
Q37
95-th percentile11
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4808725
Coefficient of variation (CV)0.37159729
Kurtosis-0.31080436
Mean6.6762395
Median Absolute Deviation (MAD)2
Skewness0.65791586
Sum636506
Variance6.1547283
MonotonicityNot monotonic
2025-11-15T12:52:34.071637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
7 29594
31.0%
5 27186
28.5%
11 12222
12.8%
3 7452
 
7.8%
4 5311
 
5.6%
8 4681
 
4.9%
12 3871
 
4.1%
6 3790
 
4.0%
10 554
 
0.6%
9 222
 
0.2%
Other values (3) 456
 
0.5%
ValueCountFrequency (%)
1 220
 
0.2%
2 190
 
0.2%
3 7452
 
7.8%
4 5311
 
5.6%
5 27186
28.5%
6 3790
 
4.0%
7 29594
31.0%
8 4681
 
4.9%
9 222
 
0.2%
10 554
 
0.6%
ValueCountFrequency (%)
13 46
 
< 0.1%
12 3871
 
4.1%
11 12222
12.8%
10 554
 
0.6%
9 222
 
0.2%
8 4681
 
4.9%
7 29594
31.0%
6 3790
 
4.0%
5 27186
28.5%
4 5311
 
5.6%

has_plus_sizes
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size93.2 KiB
False
85393 
True
9946 
ValueCountFrequency (%)
False 85393
89.6%
True 9946
 
10.4%
2025-11-15T12:52:34.209929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

price
Real number (ℝ)

Distinct54
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.010473
Minimum6.99
Maximum799.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:34.368373image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum6.99
5-th percentile12.99
Q125.99
median29.99
Q339.99
95-th percentile69.99
Maximum799.99
Range793
Interquartile range (IQR)14

Descriptive statistics

Standard deviation28.831422
Coefficient of variation (CV)0.7790071
Kurtosis135.90077
Mean37.010473
Median Absolute Deviation (MAD)10
Skewness8.0667678
Sum3528541.5
Variance831.25088
MonotonicityNot monotonic
2025-11-15T12:52:34.582075image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.99 20606
21.6%
39.99 13426
14.1%
25.99 10436
10.9%
35.99 6921
 
7.3%
49.99 6088
 
6.4%
19.99 6008
 
6.3%
22.99 5764
 
6.0%
59.99 4334
 
4.5%
15.99 2906
 
3.0%
12.99 2565
 
2.7%
Other values (44) 16285
17.1%
ValueCountFrequency (%)
6.99 50
 
0.1%
7.99 1584
 
1.7%
9.99 1720
 
1.8%
12.99 2565
 
2.7%
15.99 2906
 
3.0%
17.99 2098
 
2.2%
19.99 6008
6.3%
22.99 5764
6.0%
25.99 10436
10.9%
27.99 1991
 
2.1%
ValueCountFrequency (%)
799.99 8
 
< 0.1%
699.99 24
< 0.1%
499.99 8
 
< 0.1%
349.99 36
< 0.1%
320 6
 
< 0.1%
300 6
 
< 0.1%
299.99 48
0.1%
270 12
 
< 0.1%
269.99 12
 
< 0.1%
260 18
 
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size745.0 KiB
2023
48683 
2024
43921 
2022
 
2468
2025
 
267

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters381356
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 48683
51.1%
2024 43921
46.1%
2022 2468
 
2.6%
2025 267
 
0.3%

Length

2025-11-15T12:52:34.817351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-15T12:52:34.981799image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
2023 48683
51.1%
2024 43921
46.1%
2022 2468
 
2.6%
2025 267
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 193146
50.6%
0 95339
25.0%
3 48683
 
12.8%
4 43921
 
11.5%
5 267
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 381356
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 193146
50.6%
0 95339
25.0%
3 48683
 
12.8%
4 43921
 
11.5%
5 267
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 381356
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 193146
50.6%
0 95339
25.0%
3 48683
 
12.8%
4 43921
 
11.5%
5 267
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 381356
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 193146
50.6%
0 95339
25.0%
3 48683
 
12.8%
4 43921
 
11.5%
5 267
 
0.1%

num_week_iso
Real number (ℝ)

Distinct52
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.649535
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:35.185648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q115
median28
Q341
95-th percentile50
Maximum52
Range51
Interquartile range (IQR)26

Descriptive statistics

Standard deviation14.733438
Coefficient of variation (CV)0.53286388
Kurtosis-1.2461839
Mean27.649535
Median Absolute Deviation (MAD)13
Skewness-0.054107221
Sum2636079
Variance217.07421
MonotonicityNot monotonic
2025-11-15T12:52:35.418330image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 2329
 
2.4%
42 2294
 
2.4%
17 2293
 
2.4%
45 2282
 
2.4%
18 2258
 
2.4%
15 2248
 
2.4%
44 2248
 
2.4%
43 2219
 
2.3%
41 2214
 
2.3%
19 2204
 
2.3%
Other values (42) 72750
76.3%
ValueCountFrequency (%)
1 1329
1.4%
2 1229
1.3%
3 1329
1.4%
4 1252
1.3%
5 1432
1.5%
6 1422
1.5%
7 1480
1.6%
8 1486
1.6%
9 1634
1.7%
10 1741
1.8%
ValueCountFrequency (%)
52 1481
1.6%
51 1740
1.8%
50 1895
2.0%
49 2079
2.2%
48 2121
2.2%
47 2002
2.1%
46 2128
2.2%
45 2282
2.4%
44 2248
2.4%
43 2219
2.3%

weekly_sales
Real number (ℝ)

HIGH CORRELATION 

Distinct5009
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean948.03039
Minimum-1706
Maximum17291
Zeros307
Zeros (%)0.3%
Negative5071
Negative (%)5.3%
Memory size745.0 KiB
2025-11-15T12:52:35.650450image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1706
5-th percentile-3
Q199
median451
Q31380
95-th percentile3483.1
Maximum17291
Range18997
Interquartile range (IQR)1281

Descriptive statistics

Standard deviation1239.0753
Coefficient of variation (CV)1.3069995
Kurtosis7.9519369
Mean948.03039
Median Absolute Deviation (MAD)416
Skewness2.2844951
Sum90384269
Variance1535307.5
MonotonicityNot monotonic
2025-11-15T12:52:35.895892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 346
 
0.4%
3 337
 
0.4%
16 317
 
0.3%
1 315
 
0.3%
4 311
 
0.3%
15 311
 
0.3%
23 309
 
0.3%
7 309
 
0.3%
19 308
 
0.3%
5 308
 
0.3%
Other values (4999) 92168
96.7%
ValueCountFrequency (%)
-1706 1
< 0.1%
-1496 1
< 0.1%
-1385 1
< 0.1%
-1283 1
< 0.1%
-1157 1
< 0.1%
-1146 1
< 0.1%
-973 1
< 0.1%
-867 1
< 0.1%
-834 1
< 0.1%
-819 1
< 0.1%
ValueCountFrequency (%)
17291 1
< 0.1%
14546 1
< 0.1%
14035 1
< 0.1%
13886 1
< 0.1%
13249 1
< 0.1%
13206 1
< 0.1%
12899 1
< 0.1%
12713 1
< 0.1%
12687 1
< 0.1%
12141 1
< 0.1%

weekly_demand
Real number (ℝ)

HIGH CORRELATION 

Distinct5623
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1117.1747
Minimum-1496
Maximum38179
Zeros243
Zeros (%)0.3%
Negative3344
Negative (%)3.5%
Memory size745.0 KiB
2025-11-15T12:52:36.170252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1496
5-th percentile7
Q1124
median535
Q31584
95-th percentile4085.1
Maximum38179
Range39675
Interquartile range (IQR)1460

Descriptive statistics

Standard deviation1507.7155
Coefficient of variation (CV)1.349579
Kurtosis28.308793
Mean1117.1747
Median Absolute Deviation (MAD)484
Skewness3.2927171
Sum1.0651032 × 108
Variance2273206
MonotonicityNot monotonic
2025-11-15T12:52:36.770090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 298
 
0.3%
4 298
 
0.3%
3 291
 
0.3%
8 282
 
0.3%
15 281
 
0.3%
31 277
 
0.3%
9 275
 
0.3%
35 272
 
0.3%
26 269
 
0.3%
18 266
 
0.3%
Other values (5613) 92530
97.1%
ValueCountFrequency (%)
-1496 1
< 0.1%
-1220 1
< 0.1%
-1164 1
< 0.1%
-1157 1
< 0.1%
-957 1
< 0.1%
-819 1
< 0.1%
-815 1
< 0.1%
-784 1
< 0.1%
-780 1
< 0.1%
-756 1
< 0.1%
ValueCountFrequency (%)
38179 1
< 0.1%
36233 1
< 0.1%
35404 1
< 0.1%
32964 1
< 0.1%
31570 1
< 0.1%
30173 1
< 0.1%
27198 1
< 0.1%
26359 1
< 0.1%
26349 1
< 0.1%
24146 1
< 0.1%

Production
Real number (ℝ)

HIGH CORRELATION 

Distinct8616
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28927.421
Minimum90
Maximum403172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size745.0 KiB
2025-11-15T12:52:37.028337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile2094
Q16800
median19266
Q337426
95-th percentile90693
Maximum403172
Range403082
Interquartile range (IQR)30626

Descriptive statistics

Standard deviation34792.567
Coefficient of variation (CV)1.2027539
Kurtosis19.525629
Mean28927.421
Median Absolute Deviation (MAD)13820
Skewness3.4931329
Sum2.7579114 × 109
Variance1.2105227 × 109
MonotonicityNot monotonic
2025-11-15T12:52:37.264829image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5004 50
 
0.1%
1195 48
 
0.1%
4675 48
 
0.1%
775 48
 
0.1%
1955 48
 
0.1%
2604 47
 
< 0.1%
3518 46
 
< 0.1%
3044 45
 
< 0.1%
3430 44
 
< 0.1%
4000 44
 
< 0.1%
Other values (8606) 94871
99.5%
ValueCountFrequency (%)
90 8
< 0.1%
97 4
 
< 0.1%
119 6
< 0.1%
159 14
< 0.1%
165 6
< 0.1%
181 8
< 0.1%
192 1
 
< 0.1%
196 8
< 0.1%
203 6
< 0.1%
211 6
< 0.1%
ValueCountFrequency (%)
403172 24
< 0.1%
393921 24
< 0.1%
337245 24
< 0.1%
322732 24
< 0.1%
307031 24
< 0.1%
305404 24
< 0.1%
280388 30
< 0.1%
279473 20
< 0.1%
278602 24
< 0.1%
276971 24
< 0.1%

Interactions

2025-11-15T12:52:30.110996image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:18.238199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:19.682483image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.180859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:22.645341image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:24.154258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.036281image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:27.306131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:28.750003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:30.249793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:18.378476image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:19.850494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.338441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:22.802674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:24.304103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.189998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:27.487542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:28.923942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:30.395541image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:18.533654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:20.021343image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.496876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:22.968010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:24.477032image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.322833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:27.639350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.058383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:30.561502image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:18.701065image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:20.185930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.652745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:23.144427image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:24.626864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.458364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:27.793409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.204998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:30.739383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:18.864471image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:20.334839image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.809266image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:23.303990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:24.781858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.585410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:27.954876image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.343237image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:30.889974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:19.043828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:20.506024image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.976594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:23.471143image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:24.950145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.719197image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:28.103778image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.530499image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:31.034961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:19.182965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:20.658928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:22.131129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:23.615664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:25.111055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.843895image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:28.257194image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.671047image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:31.186260image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:19.343828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:20.831997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:22.293021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:23.788575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:25.336668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:26.987409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:28.401769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.811859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:31.327409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:19.508383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:21.004078image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:22.469611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:23.971704image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:25.528443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:27.142049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:28.556864image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2025-11-15T12:52:29.963288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2025-11-15T12:52:37.487107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
IDProductionhas_plus_sizesid_seasonlife_cycle_lengthnum_sizesnum_storesnum_week_isopriceweekly_demandweekly_salesyear
ID1.0000.0010.0330.030-0.0050.0020.0090.0080.0060.0060.0070.020
Production0.0011.0000.3380.0500.1690.7380.923-0.003-0.3290.7830.7720.030
has_plus_sizes0.0330.3381.0000.2450.1080.4390.320-0.078-0.0590.2700.2630.058
id_season0.0300.0500.2451.000-0.014-0.0990.0590.3210.002-0.018-0.0080.567
life_cycle_length-0.0050.1690.108-0.0141.0000.1590.080-0.058-0.221-0.018-0.0190.110
num_sizes0.0020.7380.439-0.0990.1591.0000.707-0.029-0.1260.5940.5760.077
num_stores0.0090.9230.3200.0590.0800.7071.0000.048-0.1990.7740.7550.061
num_week_iso0.008-0.003-0.0780.321-0.058-0.0290.0481.0000.044-0.023-0.0380.268
price0.006-0.329-0.0590.002-0.221-0.126-0.1990.0441.000-0.227-0.2350.030
weekly_demand0.0060.7830.270-0.018-0.0180.5940.774-0.023-0.2271.0000.9770.016
weekly_sales0.0070.7720.263-0.008-0.0190.5760.755-0.038-0.2350.9771.0000.038
year0.0200.0300.0580.5670.1100.0770.0610.2680.0300.0160.0381.000

Missing values

2025-11-15T12:52:31.533814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-15T12:52:31.838166image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDid_seasonheel_shape_typetoecap_typelife_cycle_lengthnum_storesnum_sizeshas_plus_sizespriceyearnum_week_isoweekly_salesweekly_demandProduction
0186NaNNaN121525False35.992023166694556
1186NaNNaN121525False35.99202321121124556
2186NaNNaN121525False35.99202331351354556
3186NaNNaN121525False35.992023499994556
4186NaNNaN121525False35.992023574744556
5186NaNNaN121525False35.992023662624556
6186NaNNaN121525False35.992023745454556
7186NaNNaN121525False35.992023838384556
8186NaNNaN121525False35.992023934344556
9186NaNNaN121525False35.9920231053534556
IDid_seasonheel_shape_typetoecap_typelife_cycle_lengthnum_storesnum_sizeshas_plus_sizespriceyearnum_week_isoweekly_salesweekly_demandProduction
953291276787NaNNaN105997False159.992023424144198959
953301276787NaNNaN105997False159.992023434714828959
953311276787NaNNaN105997False159.992023445945998959
953321276787NaNNaN105997False159.992023456016868959
953331276787NaNNaN105997False159.992023464745638959
953341276787NaNNaN105997False159.99202347-162828959
953351276787NaNNaN105997False159.992023482313248959
953361276787NaNNaN105997False159.992023494106948959
953371276787NaNNaN105997False159.992023503474418959
953381276787NaNNaN105997False159.992023511743198959